You are here: Home / Research Topics / Research Highlights / Individual Highlight

Research Highlights

Individual Highlight

ModelMap Predicts Forest Characteristics Over Any Geographic Extent

Photo of A world map displaying the density of ModelMap downloads. U.S. Department of Agriculture Forest Service.A world map displaying the density of ModelMap downloads. U.S. Department of Agriculture Forest Service.Snapshot : Forest Service scientists created a tool, ModelMap, that can combine the Forest Inventory and Analysis plot data with remote sensing satellite images to predict forest characteristics (such as species composition, crown cover, and forest disturbances) over any geographic extent.

Principal Investigators(s) :
Freeman, ElizabethFrescino, Tracey
Moisen, GretchenPatterson, Paul L.
Schleeweis, KarenToney, Chris
Research Location : International
Research Station : Rocky Mountain Research Station (RMRS)
Year : 2016
Highlight ID : 993

Summary

Working in the Forest Inventory and Analysis (FIA) Program, Forest Service scientists have access to a valuable collection of detailed information about forests on thousands of sample plots distributed across the country. This information is used to produce summaries of forestland characteristics for a variety of geographic areas such as states or individual national forests. They wanted a simple tool to extend this sample data and make detailed maps of forest characteristics for all the land in between the study locations. They began by writing code to create models and produce maps of forest and rangeland species in Nevada, using the R software environment for statistical computing and graphics. R is a powerful and flexible tool for statistical analysis, but can have a steep learning curve for new users. When the scientists realized that the tools they were developing could be useful in many different contexts in the Rocky Mountain states and throughout the country, they developed an R package, ModelMap, to gather these modeling and map making tools together, complete with help files, training vignettes, and a graphical user interface. In 2009, the package was made available for download and use by researchers worldwide through the Comprehensive R Archive Network (CRAN). Since then it has been expanded and updated 11 times, adding additional model types, and the ability to predict and map categorical data such as disturbance types. The package is currently widely used throughout the world in fields ranging from forestry to oceanography and to the health sciences.